Bayesian Mixed-effects Models
Bayesian Mixed-Effects Models is a statistical modeling framework that extends the class of mixed-effects models by incorporating Bayesian inference to estimate fixed and random effects, distinguishing itself through the use of prior distributions and posterior updating to account for uncertainty in model parameters.
319
Publications
36.3K
Citations
1.1K
Authors
605
Institutions
Publications per year
2017–2026
123
Variants
Bayesian Mixed Effects Models
Parents
319
Analyzing mixing systems using a new generation of Bayesian tracer mixing models
Brian C. Stock, Andrew L. Jackson, Eric J. Ward et al. · PeerJ · 2018 · 1.4K citations · Full text
Computing, Biostatistics, Ecology +17
1–5 of 319
1.1K
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605
University of Michigan
Ann Arbor, United States
11 h-index
16 publications
2.6K citations
University of South Florida
Tampa, United States
11 h-index
23 publications
686 citations
University of Wisconsin–Madison
Madison, United States
10 h-index
21 publications
1.1K citations
University of Zurich
Zurich, Switzerland
10 h-index
29 publications
2K citations
University of Missouri
Columbia, United States
8 h-index
20 publications
1.7K citations

Johns Hopkins University
Baltimore, United States
8 h-index
11 publications
2.8K citations
University College London
London, United Kingdom
8 h-index
21 publications
9.8K citations
The University of Texas at Austin
Austin, United States
7 h-index
13 publications
484 citations
1–8 of 605
23 publications · 1K citations
16
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13 publications · 4.6K citations
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CSDA · 13 publications · 676 citations
12
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9 publications · 632 citations
9
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7 publications · 239 citations
7
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1–5